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Scalable, human-relevant approaches for detecting compound modes of action are needed to improve chemical safety evaluation. Here, we apply image-based profiling (Cell Painting) alongside two cytotoxicity assays in primary human hepatocytes exposed to eight concentrations of 1,085 compounds spanning pharmaceuticals, pesticides, and industrial chemicals. We compared three computational approaches (CellProfiler, a Cell Painting-specific convolutional neural network, and a pretrained vision transformer) to extract morphological features from single cells or whole images. These features were used to predict activity in the measured cytotoxicity assays and in ToxCast assays covering cytotoxicity, cell-based, and cell-free assay endpoints. Morphological profiles detected bioactivity at lower concentrations than standard cytotoxicity assays and provided mode-of-action insights. In supervised analyses, they predicted cytotoxicity and targeted cell-based assays but not cell-free assays. Feature extraction methods performed similarly, and filtering concentrations did not improve performance. We envision that image-based profiling could be a key component of modern safety assessment.